'vtreat' is a package that prepares arbitrary data frames into clean data frames that are ready for analysis (usually supervised learning). A clean data frame:
To effect this encoding 'vtreat' replaces original variables or columns with new derived variables. In this note we will use variables and columns as interchangeable concepts. This note describes the current family of 'vtreat' derived variable types.
'vtreat' usage splits into three main cases:
In all cases vtreat variable names are built by appending a notation
onto the original user supplied column name. In all cases the easiest
way to examine the derived variables is to look at the scoreFrame
component
of the returned treatment plan.
We will outline each of these situations below:
An example categorical variable treatment is demonstrated below:
library(vtreat) dTrainC <- data.frame(x=c('a','a','a','b','b',NA), z=c(1,2,3,4,NA,6),y=c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE), stringsAsFactors = FALSE) treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE) scoreColsToPrint <- c('origName','varName','code','rsq','sig','extraModelDegrees') print(treatmentsC$scoreFrame[,scoreColsToPrint])
For each user supplied variable or column (in this case x
and z
) 'vtreat'
proposes derived or treated variables. The mapping
from original variable name to derived variable name is given by comparing the
columns origName
and varName
. One can map facts about the new variables
back to the original variables as follows:
# Build a map from vtreat names back to reasonable display names vmap <- as.list(treatmentsC$scoreFrame$origName) names(vmap) <- treatmentsC$scoreFrame$varName print(vmap['x_catB']) # Map significances back to original variables aggregate(sig~origName,data=treatmentsC$scoreFrame,FUN=min)
In the scoreFrame
the sig
column is the significance of the single variable logistic regression using the named variable (plus a constant term), and the rsq
column is the "pseudo-r-squared" or portion of deviance explained (please see here for some notes).
Essentially a derived variable name is built by concatenating an original variable name and
a treatment type (also recorded in the code
column for convenience). The codes
give the different 'vtreat' variable types (or really meanings, as all derived
variables are numeric).
For categorical targets the possible variable types are as follows:
x_lev_x.a
is 1 when the original x
variable had a value of "a". These indicators are essentially variables representing explicit encoding of levels as dummy variables. In some cases a special level code is used to represent pooled rare values.x_catB = logit(P[y==target|x]) - logit(P[y==target])
. This encoding is especially useful for categorical variables that have a large number of levels, but be aware it can obscure degrees of freedom if not used properly.An example numeric variable treatment is demonstrated below:
library(vtreat) dTrainN <- data.frame(x=c('a','a','a','b','b',NA), z=c(1,2,3,4,NA,6),y=as.numeric(c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE)), stringsAsFactors = FALSE) treatmentsN <- designTreatmentsN(dTrainN,colnames(dTrainN),'y') print(treatmentsN$scoreFrame[,scoreColsToPrint])
The treatment of numeric targets is similar to that of categorical targets. In the numeric case the possible derived variable types are:
x_lev_x.a
is 1 when the original x
variable had a value of "a". These indicators are essentially variables representing explicit encoding of levels as dummy variables. In some cases a special level code is used to represent pooled rare values.x_catN = E[y|x] - E[y]
. This encoding is especially useful for categorical variables that have a large number of levels, but be aware it can obscure degrees of freedom if not used properly.Note: for categorical targets we don't need cat\_D
variables as this information is
already encoded in cat\_B
variables.
In the scoreFrame
the sig
column is the significance of the single variable linear regression using the named variable (plus a constant term), and the rsq
column is the "r-squared" or portion of variance explained (please see here) for some notes).
An example "no target" variable treatment is demonstrated below:
library(vtreat) dTrainZ <- data.frame(x=c('a','a','a','b','b',NA), z=c(1,2,3,4,NA,6), stringsAsFactors = FALSE) treatmentsZ <- designTreatmentsZ(dTrainZ,colnames(dTrainZ)) print(treatmentsZ$scoreFrame[, c('origName','varName','code','extraModelDegrees')])
Note: because there is no user supplied target the scoreFrame
significance
columns are not meaningful, and are populated only for regularity of
code interface. Also indicator variables are only formed by designTreatmentsZ
for vtreat
0.5.28 or newer. Beyond that the no-target treatments are similar to the earlier treatments.
Possible derived variable types in this case are:
x_lev_x.a
is 1 when the original x
variable had a value of "a". These indicators are essentially variables representing explicit encoding of levels as dummy variables. In some cases a special level code is used to represent pooled rare values.Both designTreatmentsX
and prepare
functions take an argument called codeRestriction
that restricts the type of variable that is created. For example, you may not want to create catP
and catD
variables for a regression problem.
dTrainN <- data.frame(x=c('a','a','a','b','b',NA), z=c(1,2,3,4,NA,6),y=as.numeric(c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE)), stringsAsFactors = FALSE) treatmentsN <- designTreatmentsN(dTrainN,colnames(dTrainN),'y', codeRestriction = c('lev', 'catN', 'clean', 'isBAD'), verbose=FALSE) # no catP or catD variables print(treatmentsN$scoreFrame[,scoreColsToPrint])
Conversely, even if you have created a treatment plan for a particular type of variable, you may subsequently decide not to use it. For example, perhaps you only want to use indicator variables and not the catN
variable for modeling. You can use codeRestriction
in prepare()
.
dTreated = prepare(treatmentsN, dTrainN, codeRestriction = c('lev','clean', 'isBAD')) # no catN variables head(dTreated)
varRestriction
works similarly, only you must list the explicit variables to use. See the example below.
Variables that "do not move" (don't take on at least two values during treatment design)
or don't achieve at least a minimal significance are suppressed. The catB
/catN
variables
are essentially single variable models and are very useful for re-encoding categorical
variables that take on a very large number of values (such as zip-codes).
The intended use of 'vtreat' is as follows:
'vtreat' attempts to compute "out of sample" significances for each variable effect (
the sig
column in scoreFrame
) through cross-validation techniques.
'vtreat' is primarily intended to be "y-aware" processing. Of particular interest is
using vtreat::prepare()
with scale=TRUE
which tries to put most columns in 'y-effect'
units. This can be an important pre-processing step before attempting dimension reduction
(such as principal components methods).
The vtreat user should pick which sorts of variables they are want and also filter on estimated significance. Doing this looks like the following:
dTrainN <- data.frame(x=c('a','a','a','b','b',NA), z=c(1,2,3,4,NA,6),y=as.numeric(c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE)), stringsAsFactors = FALSE) treatmentsN <- designTreatmentsN(dTrainN,colnames(dTrainN),'y', codeRestriction = c('lev', 'catN', 'clean', 'isBAD'), verbose=FALSE) print(treatmentsN$scoreFrame[,scoreColsToPrint]) pruneSig <- 1.0 # don't filter on significance for this tiny example vScoreFrame <- treatmentsN$scoreFrame varsToUse <- vScoreFrame$varName[(vScoreFrame$sig<=pruneSig)] print(varsToUse) origVarNames <- sort(unique(vScoreFrame$origName[vScoreFrame$varName %in% varsToUse])) print(origVarNames) # prepare a treated data frame using only the "significant" variables dTreated = prepare(treatmentsN, dTrainN, varRestriction = varsToUse) head(dTreated)
We strongly suggest using the standard variables coded as 'lev', 'clean', and 'isBad'; and the "y aware" variables coded as 'catN' and 'catB'. The non sub-model variables ('catP' and 'catD') can be useful (possibly as interactions or guards on the corresponding 'catN' and 'catB' variables) but also encode distributional facts about the data that may or may not be appropriate depending on your problem domain.
When displaying variables to end users we suggest using the original names and the min significance seen on any derived variable:
origVarNames <- sort(unique(vScoreFrame$origName[vScoreFrame$varName %in% varsToUse])) print(origVarNames) origVarSigs <- vScoreFrame[vScoreFrame$varName %in% varsToUse,] aggregate(sig~origName,data=origVarSigs,FUN=min)
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